1	"Luís Orlindo Tedeschi" Luís Orlindo Tedeschi Assistant Professor Texas A&M University
2	Why Nutrition Models ?
3	Knowledge in the NRC pubs
4	Feeds and feeding
5	Environmental (clime) effects
6	Production systems (dairy)
7	Production systems (beef)
8	Application of models
9
10	Objectives of the presentation
11	Step 1 – Predicting requirements
12	Step 2 – Estimating supply
13
14
15
16	"1" 1. Maintenance
17	Factors affecting maintenance Heat or Cold stress External Insulation Coat Condition Wind speed Hide Thickness Internal Insulation Condition Score Age Body weight Physiological State Dry Lactating Compensating Acclimatization Previous temperature
18
19	Maintenance requirements Dairy Heifers = SBW^0.75 x 0.086, Mcal/d Dairy Cows = SBW^0.75 x 0.080, Mcal/d Beef = SBW^0.75 x 0.077, Mcal/d Include basal metabolism + 10% for physical activity
20	Environmental (clime) effects
21	"2" 2. Growth
22	NEg required in the US Genotype - over 80 types have been identified Sex Feedlot steers, heifers & bulls Replacement heifers Bulls Cows Implant combinations Feeding systems
23	Breeds effect
24	Nutrition & feeding levels
25	Setting target body fat
26	Breed type vs. body fat
27	Predicting growth requirement
28	Protein composition
29	Fat composition
30	Calculation of EqSBW to a SRW EqSBW = Actual SBW x (SRW / FW) SRW: 435 kg @ 25% EBF 462 kg @ 27% EBF 478 kg @ 28% EBF
31
32	Net energy requirement Final weight: 478 kg A B Weight, kg 227 408 NEm, Mcal/d 4.51 7.00 Final weight: 667 kg C D Weight, kg 324 583 NEm, Mcal/d 5.89 9.15 NEg, Mcal/d 0.68 kg/d 2.14 3.32 1.59 kg/d 5.42 8.42
33	"3" 3. Body Reserves
34	Changes in body reserves
35
36	Energy reserves @ different BCS
37	"4" 4. Pregnancy and Lactation
38	Pregnancy and lactation
39	Requirements for pregnancy
40	Requirements for lactation
41	"1" 1. Rumen: Fractionation
42	CHO and protein fractionation
43	Chemical feed analysis Crude protein (CP) Soluble CP (SolCP) Non-protein N (NPN) NDF Protein (NDIN) ADF Protein (ADIN) Dry matter (DM) Ash NDF Ether extract (EE) Lignin Starch
44
45
46	"2" 2. Rumen: Degradation
47	Rumen degradation Assumptions in the model: Steady-state condition (dCHO B/dt = 0) Linear relationship between flows and stocks
48	Feed dynamics in the rumen
49	"3" 3. Rumen: Effective NDF
50
51	Physically effective NDF (peNDF)
52
53	Physical effectiveness of grains
54	Observed vs. predicted chewing
55
56
57	Using Z-Box to predict peNDF Z-Box system promising for determination of pef of “as fed” samples Vigorous vertical shaking 150 g/sample (3 - 50 g/replicates) pef Z-Box similar to pef_1.18 CS and TMR: 3.18-mm sieve Haylage: 4.76-mm sieve Use different sieves for different feed types
58	"4" 4. Bacteria
59	Modeling ruminal bacteria growth
60	Modeling ruminal bacteria growth Depends on requirement of CHO for maintenance (km), maximum yield (Y_g), and kd Km₁ (FC bacteria) = 0.05 g FC/g bact/h Km₂ (NFC bacteria) = 0.15 g NFC/g bact/h Y_g affected by peNDF < 20%; 0.4 g bact/g CHO
61	Bacteria yield: FC x NFC
62	NFC bacteria yield
63	"5" 5. Intestine
64	Intestinal digestibility coefficients Based on Sniffen et al. (1992) and Knowlton et al. (1998) Protein A, B1 and B2 = 100% B3 = 80% C = 0% Carbohydrate B2 (NDF) = 20% due to lack of proper enzymes B1 (Starch) based on observation of the feces and in adjusting inputs to account for predicted and actual animal performance
65	Starch (B1) Intestinal Digestibility
66	Intestinal digestibilities
67
68	Future modeling research
69
70	Pattern of DMI
71	Fluxes of body reserves
72	2. Dynamic rumen model Basis for VFA and AA system Synchronization of energy-protein Methane production Pool size
73	Beef cattle ruminal pH
74	Beef cattle ruminal pH
75	3. Ruminant dry matter intake DMI indirectly dictates the profile of VFA produced in the rumen via acid load and pH DMI is not controlled by one specific mechanism, but by a multifactorial system, which seeks for a balance Need to understand the behavior of DMI
76	Scheme of feed intake control
77
78	4. Passage rate model Seo et al. (2006) equations were the best among a total of 8 tested equations However, the predictability was still low Forage passage R² = 39% RMSPE = 0.011 h^-1 Liquid passage R² = 25% RMSPE = 0.033 h^-1
79	Quantification of digesta outflow Digesta outflow is a function of: Frequency and duration of the ROO opening Digesta flow per second of the ROO opening
80	Liquid dynamics model
81	Prediction for Kp Liquid
82	Prediction for Kp Forage
83	Summary
84